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Awesome List of Controls, Vision, Planning
https://github.com/aswathselvam/awesome-list
List: awesome-list
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Awesome List of Controls, Vision, Planning
Host: GitHub
URL: https://github.com/aswathselvam/awesome-list
Owner: aswathselvam
Created: 2021-12-28T16:24:13.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-11-13T04:55:44.000Z (almost 2 years ago)
Last Synced: 2024-04-21T05:11:22.338Z (5 months ago)
Size: 88.9 KB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - awesome-list - Awesome List of Controls, Vision, Planning. (Other Lists / PowerShell Lists)
README

        # awesome-list

Awesome List of Controls, Vision, Planning

## Interdisciplinary

**Repositories from Industries**

1. [Google](https://github.com/google-research)

2. [Meta](https://github.com/facebookresearch)

3. [Snap](https://github.com/snap-research)

4. [NASA](https://github.com/nasa)

5. [Deepmind](https://github.com/deepmind/deepmind-research)

6. [Qualcomm](https://github.com/Qualcomm-AI-research)

7. [Microsoft](https://github.com/microsoft/EdgeML)

8. [Intel](https://github.com/IntelAI)

9. [Toyota Research Institute](https://github.com/orgs/TRI-ML/repositories)

10. [Magic Leap](https://github.com/magicleap)

11. [Neural Magic](https://github.com/neuralmagic)

12. [Tangram Vision](https://gitlab.com/tangram-vision/oss)

**Repositories from Academia**

1. [Robotic Systems Lab](https://github.com/leggedrobotics)

2. [Autonomous Systems Lab](https://github.com/StanfordASL), [torchfilter](https://github.com/stanford-iprl-lab/torchfilter)

3. [Air Lab](https://github.com/castacks)

4. [xLab for Safe Autonomous Systems](https://github.com/mlab-upenn)

**Research**

1. https://github.com/MLNLP-World/Top-AI-Conferences-Paper-with-Code

2. [AI Conference Deadlines](http://aideadlin.es/?sub=RO,CV)

3. [papers.labml.ai](https://papers.labml.ai/papers/weekly/))

4. [AAAI](https://aaai.org/)

5. https://github.com/Hippogriff/ML-Resources/blob/master/courses.org

6. [MATH-GA 2821 Optimization-based Data Analysis](https://cims.nyu.edu/~cfgranda/pages/OBDA_fall17/schedule.html)

**People**

1. [Tom Goldstein](https://www.cs.umd.edu/~tomg/)

2. [Haizhao Yang](https://sites.google.com/prod/view/haizhaoyang/research)

3. [Ming C. Lin](http://www.cs.umd.edu/~lin/)

4. [Maria K. Cameron](https://www.math.umd.edu/~mariakc/) - Math

**Companies**

1. [Microsoft Research](https://www.microsoft.com/en-us/research/)

## Vision

1. [Google Scholar: Top Vision conferences](https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_computervisionpatternrecognition)

2. [List of Conferences and Deadline](https://vision.ai.illinois.edu/links/)

3. [CVF](https://www.thecvf.com/), [CVF Open Access](https://openaccess.thecvf.com/menu), [Computer Vision Awards](https://www.thecvf.com/?page_id=413)

4. https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

5. https://cmsc733.github.io/2019/proj/p3/

6. https://github.com/anirudhtopiwala/ENPM-673-Perception-for-Autonomous-Robots

7. https://github.com/SilenceOverflow/Awesome-SLAM

8. https://github.com/thien94/Another_VO_SLAM_List

9. https://github.com/dectrfov/awesome_3DReconstruction_list

10. [Objectron](https://github.com/google-research-datasets/Objectron)

11. Shape for shading: https://github.com/hongzimao/shapeFromShading

12. [Shape Manifolds lecture slides - basics](https://github.com/tomfletcher/GeometryOfData)

13. [Classical Vision - Books](https://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm) 💥

14. [3D Machine Learning](https://github.com/timzhang642/3D-Machine-Learning), [Holistic 3D Reconstruction](https://github.com/holistic-3d/awesome-holistic-3d)

**People**

1. https://mengzephyr.com/

2. https://www.mmlab-ntu.com/person/ccloy/publication_topic.html

**Institutes**

1. MIT [6.801/6.866 Machine Vision](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2020/), 

[2004](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2004/)

2. MIT [6.819/6.869: Advances in Computer Vision](http://6.869.csail.mit.edu/sp21/). Example [Repository](https://github.com/akselsd/MIT-6.869-Advances-In-Computer-Vision)

3. UT Austin - [GAMES Advanced Course 3D Reconstruction and Understanding](https://www.cs.utexas.edu/~huangqx/Games_3D_Recons_Understanding.html)

4. CMU [16-721 Learning-Based Methods in Vision](http://www.cs.cmu.edu/~efros/courses/LBMV07/)

5. Stanford [CS231n Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/)

6. Stanford [EE367 / CS448I: Computational Imaging](http://stanford.edu/class/ee367/)

7. Stanford [Convex Optimization Short Course](https://web.stanford.edu/~boyd/papers/cvx_short_course.html), [Total Variation in-Painting](https://nbviewer.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/tv_inpainting.ipynb)

8. MIT [6.838: Shape Analysis (Spring 2021)](https://groups.csail.mit.edu/gdpgroup/6838_spring_2021.html)

9. Ideas, Problem statements from [EPFL](https://www.epfl.ch/labs/ivrl/available-projects/). 💯

10. [Oxford Active Vision Laboratory - code](https://code.active.vision/)

**Companies**

1. https://research.adobe.com/publications/

## Controls

1. [Google Scholar: Top Conrols conferences](https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_automationcontroltheory)

2. [The Impact of Control Technology - 2nd edition](http://ieeecss.org/index.php/impact-control-technology-2nd-edition)

**People**

1. [S. Shankar Sastry](https://www2.eecs.berkeley.edu/Faculty/Homepages/sastry.html)

2. [Pieter Abbeel](https://www2.eecs.berkeley.edu/Faculty/Homepages/abbeel.html)

3. [Sanjay Lall](http://lall.stanford.edu/)

4. [Boyd](https://web.stanford.edu/~boyd/index.html)

5. [Krishna Prasad](https://user.eng.umd.edu/~krishna/teaching.htm)

6. [Miroslav Krstic](http://flyingv.ucsd.edu/)

**Institutes**

1. MIT [6.800/6.843 Robotic Manipulation](https://manipulation.csail.mit.edu/Fall2021/)

2. MIT [6.832: Underactuated Robotics](http://underactuated.csail.mit.edu/Spring2021/index.html). [Further Materials](http://underactuated.csail.mit.edu/Spring2021/resources.html#further_material)

3. MIT 16.332 Formal Methods for Safe Autonomous Systems

4. MIT 16.338[J] Dynamic Systems and Control

5. MIT 16.31/16.30 Feedback Control Systems

6. MIT 16.32 Principles of Optimal Control and Estimation

7. MIT 16.343 Spacecraft and Aircraft Sensors and Instrumentation

8. MIT 16.346 Astrodynamics

9. Stanford [EE263: Introduction to Linear Dynamical Systems](http://ee263.stanford.edu/lectures.html) -> great slides on math background. 

10. UMD [ENEE 769R - Advanced Topics in Control, Principles and Algorithms for Collectives: from Biology to Robotics](http://classweb.ece.umd.edu/enee769r.F2012/)

11. UMD [ENEE 660 - System Theory](http://classweb.ece.umd.edu/enee660.F2010/)

12. UMD [ENEE 620: Random Processes in Communications and Control](https://user.eng.umd.edu/~abarg/620/)

13. UMD [CMSC 764 | ADVANCED NUMERICAL OPTIMIZATION](https://www.cs.umd.edu/~tomg/cmsc764_2020/)

**Stohastic Control**

1. Stanford [EE365: Stochastic Control](https://stanford.edu/class/ee365/index.html)

**Non-Linear**

1. UMD [ENEE 661 - Nonlinear Control Systems](http://classweb.ece.umd.edu/enee661.S2020/)

**Optimal**

1. https://github.com/ToniRV/MIT-16.32-Autonomous-Drone-Racing

**Adaptive**

1. https://github.com/aleixpb2/2.153-adaptive-controller-quadrotor

2. UMD [ENEE 765 - Adaptive Control (and Learning Theory)](http://classweb.ece.umd.edu/enee765.F2019/)

**Swarm**

1. https://github.com/yangliu28/swarm_formation_sim

2. https://github.com/yxiao1996/SwarmSim

3. https://github.com/gnotomista/swarm_sim

4. https://github.com/eshimelis/info_control

Other links:

1. https://github.com/SergeiSa/Control-Theory-Slides-Spring-2021

## Planning

1. [Google Scholar: Top Game theory/Decision science conferences](https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_gametheorydecisionscience)

**Institutes**

1. MIT 6.413[J]/6.877[J] Principles of Autonomy and Decision Making

2. MIT 16.410[J]/6.817[J] Principles of Autonomy and Decision Making

3. MIT 16.420 Planning Under Uncertainty

4. MIT 16.485 Visual Navigation for Autonomous Vehicles

**Conferences**

1. https://www.icaps-conference.org/

## Learning

1. [CMSC 828W: Foundations of Deep Learning](https://www.cs.umd.edu/~sfeizi/Teaching.html)

2. [CS 6789: Foundations of Reinforcement Learning](https://wensun.github.io/CS6789_fall_2021.html) 💥

## Robotics

1. [Google Scholar: Top Robotics conferences](https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_robotics)

2. [Arxiv CS:Robotics](https://arxiv.org/list/cs.RO/recent)

3. [Google Brain Robotics team](https://research.google/teams/brain/robotics/)

4. [Nikolay Atanasov](https://existentialrobotics.org/pages/research.html)

5. https://github.com/jslee02/awesome-robotics-libraries

6. https://github.com/mathworks-robotics/awesome-matlab-robotics

7. MIT [6.808[J] Mobile and Sensor Computing](https://6808.github.io/)

8. https://github.com/dectrfov/ICRA2021PaperList

**People**

1. https://www.mit.edu/~arosinol/

2. [Daniela Rus](https://scholar.google.com/citations?user=910z20QAAAAJ&hl=en)

**Institutes**

1. https://www.iris.ethz.ch/

2. https://rsl.ethz.ch/research/researchtopics.html

**Companies**

1. https://deepmind.com/research

2. https://www.merl.com/research/

## Self-Driving cars

1. [NuTonomy](https://github.com/nutonomy)

2. [Waymo](https://github.com/waymo-research)

3. [Lyft](https://github.com/lyft)

4. University of Tübingen [Lecture: Self-Driving Cars](https://uni-tuebingen.de/de/123611)

## Embedded

1. MIT 6.846 Parallel Computing

2. MIT 6.816/6.836 Multicore Programming()

3. MIT [6.827 Algorithm Engineering](https://people.csail.mit.edu/jshun/6827-s22/)

4. MIT 6.850 Geometric Computing

5. MIT 16.35 Real-Time Systems and Software

6. MIT [6.823 Computer System Architecture](http://csg.csail.mit.edu/6.823/lecnotes.html)

7. MIT [6.818 Dynamic Computer Language Engineering](http://6.s081.scripts.mit.edu/sp18/schedule.html)

8. MIT [18.337J/6.338J: Parallel Computing and Scientific Machine Learning](https://book.sciml.ai/). [Collection :star:](https://github.com/mitmath/18337)

9. UMD [ENEE 447 - Operating Systems by B. Jacob](http://classweb.ece.umd.edu/enee447.S2021/)

## GPU

1. [Nvidia - CUDA Samples](https://github.com/NVIDIA/cuda-samples/tree/master/Samples)

2. [CUDA C++ Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html)

3. [CMake CUDA-ToolKit](https://cmake.org/cmake/help/v3.17/module/FindCUDAToolkit.html)

4. https://rapids.ai/

5. [An introduction to GPU computing](https://lsi2.ugr.es/jmantas/ppr/teoria/descargas/PPR_CUDA.pdf) - Slides

## Tools

1. [Connected Papers](https://www.connectedpapers.com/)

## Software Engineering Practices

1. [Design Patterns](https://refactoring.guru/design-patterns/catalog)

2. [CPPCon](https://github.com/CppCon)

## Stuffs:

1. [Gang of Four Design Patterns](https://github.com/Junzhuodu/design-patterns)

2. Cornell [Reuleaux Kinematic Mechanisms Collection](https://digital.library.cornell.edu/collections/kmoddl)